Kinetic proofreading at single molecular level: aminoacylation of tRNA(Ile) and the role of water as an editor

Fluctuation theory of immune response: A statistical mechanical approach to understand pathogen induced T-cell population dynamics

In this period of intense interest in human immunity, we attempt here to quantify the immune response against pathogen invasion through T-cell population dynamics. Borrowing concepts from equilibrium statistical mechanics, we introduce a new description of the immune response function (IMRF) in terms of fluctuations in the population number of relevant biological cells (effector and regulatory T-cells). We use a coarse-grained chemical reaction network model (CG-CRNM) to calculate the number fluctuations and show that the response function derived as such can, indeed, capture the crossover observed in a T-cell driven immune response. We employ the network model to learn the effect of vitamin-D as an immunomodulator. We solve our CG-CRNM using a stochastic Gillespie algorithm. Depending on the effector T-cell concentration, we can classify immune regulation regimes into three categories: weak, strong, and moderate. The IMRF is found to behave differently in these three regimes. A damped cross-regulatory behavior found in the dynamics of effector and regulatory T-cell concentration in the diseased states correlates well with the same found in a cohort of patients with specific malignancies and autoimmune diseases. Importantly, the crossover from the weakly regulated steady state to the other (the strongly regulated) is accompanied by a divergence-like growth in the fluctuation of both the effector and the regulatory T-cell concentration, characteristic of a dynamic phase transition. We believe such steady-state IMRF analyses could help not only to phase-separate different immune stages but also aid in the valuable connection between autoimmunity, optimal vitamin-D, and consequences of immunosuppressive stress and malignancy.

Anomalous water dynamics at surfaces and interfaces: synergistic effects of confinement and surface interactions

In nature, water is often found in contact with surfaces that are extended on the scale of molecule size but small on a macroscopic scale. Examples include lipid bilayers and reverse micelles as well as biomolecules like proteins, DNA and zeolites, to name a few. While the presence of surfaces and interfaces interrupts the continuous hydrogen bond network of liquid water, confinement on a mesoscopic scale introduces new features. Even when extended on a molecular scale, natural and biological surfaces often have features (like charge, hydrophobicity) that vary on the scale of the molecular diameter of water. As a result, many new and exotic features, which are not seen in the bulk, appear in the dynamics of water close to the surface. These different behaviors bear the signature of both water-surface interactions and of confinement. In other words, the altered properties are the result of the synergistic effects of surface-water interactions and confinement. Ultrafast spectroscopy, theoretical modeling and computer simulations together form powerful synergistic approaches towards an understanding of the properties of confined water in such systems as nanocavities, reverse micelles (RMs), water inside and outside biomolecules like proteins and DNA, and also between two hydrophobic walls. We shall review the experimental results and place them in the context of theory and simulations. For water confined within RMs, we discuss the possible interference effects propagating from opposite surfaces. Similar interference is found to give rise to an effective attractive force between two hydrophobic surfaces immersed and kept fixed at a separation of d, with the force showing an exponential dependence on this distance. For protein and DNA hydration, we shall examine a multitude of timescales that arise from frustration effects due to the inherent heterogeneity of these surfaces. We pay particular attention to the role of orientational correlations and modification of the same due to interaction with the surfaces.

A stochastic chemical dynamic approach to correlate autoimmunity and optimal vitamin-D range

Motivated by several recent experimental observations that vitamin-D could interact with antigen presenting cells (APCs) and T-lymphocyte cells (T-cells) to promote and to regulate different stages of immune response, we developed a coarse grained but general kinetic model in an attempt to capture the role of vitamin-D in immunomodulatory responses. Our kinetic model, developed using the ideas of chemical network theory, leads to a system of nine coupled equations that we solve both by direct and by stochastic (Gillespie) methods. Both the analyses consistently provide detail information on the dependence of immune response to the variation of critical rate parameters. We find that although vitamin-D plays a negligible role in the initial immune response, it exerts a profound influence in the long term, especially in helping the system to achieve a new, stable steady state. The study explores the role of vitamin-D in preserving an observed bistability in the phase diagram (spanned by system parameters) of immune regulation, thus allowing the response to tolerate a wide range of pathogenic stimulation which could help in resisting autoimmune diseases. We also study how vitamin-D affects the time dependent population of dendritic cells that connect between innate and adaptive immune responses. Variations in dose dependent response of anti-inflammatory and pro-inflammatory T-cell populations to vitamin-D correlate well with recent experimental results. Our kinetic model allows for an estimation of the range of optimum level of vitamin-D required for smooth functioning of the immune system and for control of both hyper-regulation and inflammation. Most importantly, the present study reveals that an overdose or toxic level of vitamin-D or any steroid analogue could give rise to too large a tolerant response, leading to an inefficacy in adaptive immune function.